Noise Correlation Matrix Estimation for Multi-Microphone Speech Enhancement

For speech enhancement or blind signal extraction (BSE), estimating interference and noise characteristics is decisive for its performance. For multichannel approaches using multiple microphone signals, a BSE scheme combining a blocking matrix (BM) and spectral enhancement filters was proposed in numerous publications. For such schemes, the BM provides a noise estimate by suppressing the target signal only. The estimated noise reference is then used to design spectral enhancement filters for the purpose of noise reduction. For designing the BM, 'Directional Blind Source Separation (BSS)' was already proposed earlier. This method combines a generic BSS algorithm with a geometric constraint derived from prior information on the target source position to obtain an estimate for all interfering point sources and diffuse background noise. In this paper, we provide a theoretical analysis to show that Directional BSS converges to a relative transfer function (RTF)-based BM. The behavior of this informed signal separation scheme is analyzed and the blocking performance of Directional BSS under various acoustical conditions is evaluated. The robustness of Directional BSS regarding the localization error for the target source position is verified by experiments. Finally, a BSE scheme combining Directional BSS and Wiener-type spectral enhancement filters is described and evaluated.

Section: Introductionmentioning

confidence: 99%

Analysis of dual-channel ICA-based blocking matrix for improved noise estimation

Zheng

Reindl

Kellermann

2014

“…In the past decade or so, most efforts in relation to noise reduction seem to have been devoted to tracking of noise power spectral densities [11][12][13][14] to allow for better noise reduction during speech activity, extensions of noise reduction methods to multiple channels [15][16][17][18], and improved optimal filtering techniques for noise reduction [1,8,[19][20][21]. However, little progress has been made on subspace methods.…”

Section: Introductionmentioning

confidence: 99%

Single-channel noise reduction using unified joint diagonalization and optimal filtering

Nørholm

Benesty

Jensen

et al. 2014

In this paper, the important problem of single-channel noise reduction is treated from a new perspective. The problem is posed as a filtering problem based on joint diagonalization of the covariance matrices of the desired and noise signals. More specifically, the eigenvectors from the joint diagonalization corresponding to the least significant eigenvalues are used to form a filter, which effectively estimates the noise when applied to the observed signal. This estimate is then subtracted from the observed signal to form an estimate of the desired signal, i.e., the speech signal. In doing this, we consider two cases, where, respectively, no distortion and distortion are incurred on the desired signal. The former can be achieved when the covariance matrix of the desired signal is rank deficient, which is the case, for example, for voiced speech. In the latter case, the covariance matrix of the desired signal is full rank, as is the case, for example, in unvoiced speech. Here, the amount of distortion incurred is controlled via a simple, integer parameter, and the more distortion allowed, the higher the output signal-to-noise ratio (SNR). Simulations demonstrate the properties of the two solutions. In the distortionless case, the proposed filter achieves only a slightly worse output SNR, compared to the Wiener filter, along with no signal distortion. Moreover, when distortion is allowed, it is possible to achieve higher output SNRs compared to the Wiener filter. Alternatively, when a lower output SNR is accepted, a filter with less signal distortion than the Wiener filter can be constructed.

“…All of the derived filters are based on secondorder statistics of the observed signal as well as the noise. While estimation of these statistics are not considered herein, there exist multiple, well-known methods for conducting this estimation in practice both in single-channel [8,9] and multichannel [10][11][12] scenarios. Finally, we then proceed to demonstrate and discuss their application in various settings, including time and frequency domain enhancement and single-and multichannel enhancement.…”

Section: Introductionmentioning

confidence: 99%

A brief overview of speech enhancement with linear filtering

Benesty

Christensen

Jensen

et al. 2014

In this paper, we provide an overview of some recently introduced principles and ideas for speech enhancement with linear filtering and explore how these are related and how they can be used in various applications. This is done in a general framework where the speech enhancement problem is stated as a signal vector estimation problem, i.e., with a filter matrix, where the estimate is obtained by means of a matrix-vector product of the filter matrix and the noisy signal vector. In this framework, minimum distortion, minimum variance distortionless response (MVDR), tradeoff, maximum signal-to-noise ratio (SNR), and Wiener filters are derived from the conventional speech enhancement approach and the recently introduced orthogonal decomposition approach. For each of the filters, we derive their properties in terms of output SNR and speech distortion. We then demonstrate how the ideas can be applied to single-and multichannel noise reduction in both the time and frequency domains as well as binaural noise reduction.